The advent of large multi-media collections and digital libraries has led to a need for good search tools to index and retrieve information from them. For text available in machine readable form a number of good search tools are available. However, there are as yet no adequate tools to index and retrieve images. A traditional approach to searching and indexing images uses manual textual annotations, but this approach is slow, labor intensive and expensive. In addition, textual annotations cannot encode all the information available in an image. There is thus a need for retrieving images using their content directly.
A person using an image retrieval system may seek to find semantic information. For example, a person may be looking for a picture of a leopard from a certain viewpoint. Or alternatively, the user may require a picture of Abraham Lincoln. Identifying semantic information in images required for these types of queries is difficult. For example, automatic indexing for semantic retrieval typically includes, among other steps, automatic segmentation of an image into objects, which in itself is difficult.
Other approaches to image retrieval have tried to characterize the appearance of an object using a transformation of the intensity at points in an image. In one approach, an image is treated as a fixed-length vector of its pixel values. A reduced-dimension vector is then computed by projecting the vector of an image onto eigenvectors corresponding to the largest eigenvalues (principal component dimensions) computed from vectors of a set of database images. A reduced-dimension vector is computed for a query image and for each of the database images. Database images with similar (for example, close in Euclidean distance) vectors to that of the query image are returned. For this approach to be successful, all the images should be normalized to reduce variation in geometry and intensity, as well as be consistently segmented. In addition, the eigenvectors must in general be computed separately for different types of images, for example, eigenvectors computed for images of faces may not be useful for retrieving images of automobiles.